Advanced Signal Processing Methods for Analysis of Fibrillatory Waves

用于分析颤动波的先进信号处理方法

• 批准号: RGPIN-2018-05540
• 负责人: Hashemi, Javad
• 金额: $ 2.04万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750621
• 关键词: Advanced; Signal; Processing; Methods; Analysis

Signal processing methods have been used for decades to extract critical information from electrogram recordings of the human heart for diagnosis of numerous heart diseases including atrial fibrillation (AF). AF is the most common cardiac arrhythmia affecting more than 34 million people with an annual cost of $6 billion just in North America. Unfortunately, the current processing methods have had limited success in targeting the sources of AF due to the complex and dynamic nature of the disease. The conventional processing methods used for AF diagnosis are based on sequential data collection and often utilize the local information from individual recording channels to localize AF sources without considering temporal association between the channels to extract wave propagation characteristics. This can be attributed to the fact that the recordings obtained from these systems have low spatial resolution and, due to the sequential acquisition, a global snapshot of fibrillatory waves in atria is not feasible. Recent efforts focus on employing alternative recording equipment to create a panoramic view of the fibrillatory wave propagation in atria. However, the reported outcomes of these studies have been often contradictory. In addition, the high cost and difficulty in placement and maneuvering of these recording devices into small chambers of the heart have prohibited the widespread use of these new diagnostic systems. The aim of my research program is to develop new signal processing methods to study fibrillatory wave propagation in the human heart using data obtained from the conventional recording systems. This will be achieved by developing methods for accurate estimation of local activation intervals and by analyzing the temporal association of local active intervals among simultaneously recorded signals to obtain regional information, in contrast to local information. Although the regional information cannot provide a global wave propagation map in atria, it has an unexplored potential to reveal footprints of the trajectory of the fibrillatory waves and the underlying physiologic properties of the regions within the atria. Our processing methods will be developed and validated by generating realistic simulated data from a detailed 3D model. The applicability of our methods on a clinical database will also be studied. To achieve the goals of this research program, a number of projects will be conducted by five graduate students under my supervision.Successful results from this research program will provide a unique approach to investigate and better understand the propagation of fibrillatory waves using simulated/real sequential data and will facilitate a subsequent study to validate the findings on human data. This can lead to shorter clinical procedures, reduce the financial burden on the Canadian health care system and help the millions of people whose lives are affected by AF.

几十年来，信号处理方法一直被用来从人类心脏的心电记录中提取关键信息，用于诊断包括心房颤动(AF)在内的多种心脏疾病。房颤是最常见的心律失常，仅在北美每年就造成60亿美元的损失，影响着3400万人。不幸的是，由于疾病的复杂和动态性质，目前的处理方法在靶向房颤来源方面的成功有限。用于房颤诊断的常规处理方法基于顺序数据收集，并且经常利用来自各个记录通道的局部信息来定位房颤源，而不考虑通道之间的时间关联来提取波传播特征。这可以归因于这样一个事实，即从这些系统获得的记录具有较低的空间分辨率，并且由于顺序采集，不可能获得心房内纤颤波的全局快照。最近的努力集中在使用替代记录设备来创建心房内纤颤波传播的全景图。然而，这些研究的报道结果往往是相互矛盾的。此外，这些记录设备在心脏小腔内的放置和操作的高成本和困难阻碍了这些新的诊断系统的广泛使用。我的研究项目的目的是开发新的信号处理方法，利用从传统记录系统获得的数据来研究纤颤波在人体心脏中的传播。这将通过开发准确估计局部激活间隔的方法以及通过分析同时记录的信号之间的局部激活间隔的时间关联以获得与局部信息相反的区域信息来实现。虽然局部信息不能提供心房内的整体波传播图，但它具有揭示房颤波轨迹和心房内各区域潜在的生理特性的未开发的潜力。我们的处理方法将通过从详细的3D模型生成逼真的模拟数据来开发和验证。我们的方法在临床数据库上的适用性也将被研究。为了实现这项研究计划的目标，我将在五名研究生的指导下进行一些项目。这项研究计划的成功结果将提供一种独特的方法，利用模拟/真实的顺序数据来调查和更好地了解纤颤波的传播，并将促进后续研究，以验证基于人体数据的研究结果。这可以缩短临床程序，减轻加拿大医疗保健系统的经济负担，并帮助数百万受房颤影响的人。